Out of office management

ABSTRACT

Out of office management is provided to an institution and customers of the institution. Employee data can be monitored of one or more employees of the institution. A likelihood an employee is out of office is determined. A set of projects of the employee that are affected by the out of office is determined. The projects are analyzed to determine departure scores for each project of the set of projects. The projects are ranked according to the departure scores to determine criticality of each project. The projects are reassigned based on the employee data and the ranking. Customer sentiments are monitored after reassignment for machine learning to affect future out of office reassignments.

BACKGROUND

Increasingly, businesses, financial institutions, and other entitieslose customers and money due to employee vacations and other out ofoffice reasons. Customers can have projects that are time sensitivewhich can lead to the customer moving to a competitor. Employeesoftentimes have close working relationships with the customers whileworking on a project. A break point in the seamless communication withthe employee for a specific span of time leads to customer frustration,ambiguity, and stronger market competition from competitors. Preventionof customer decisions at this point can lead to losing the customer andhence business for the institution. One of the major breakpointsidentified is when the employee takes a vacation, or personal time off.

BRIEF SUMMARY OF THE DESCRIPTION

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the innovation. Thissummary is not an extensive overview of the innovation. It is notintended to identify key/critical elements of the innovation or todelineate the scope of the innovation. Its sole purpose is to presentsome concepts of the innovation in a simplified form as a prelude to themore detailed description that is presented later.

Out of office management is provided to an institution. Employee datacan be monitored of one or more employees of the institution. Alikelihood an employee is out of office is determined. A set of projectsof the employee that are affected by the out of office is determined.The projects are analyzed to determine departure scores for each projectof the set of projects. The projects are ranked according to thedeparture scores to determine criticality of each project. The projectsare reassigned based on the employee data and the ranking. Customersentiments are monitored after reassignment for machine learning toaffect future out of office reassignments.

In aspects, the subject innovation provides substantial benefits interms of out of office management. One advantage resides in automaticreassignment of projects. Another advantage resides in better retentionof customers while an employee is out of office.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the innovation are described herein inconnection with the following description and the annexed drawings.These aspects are indicative, however, of but a few of the various waysin which the principles of the innovation can be employed and thesubject innovation is intended to include all such aspects and theirequivalents. Other advantages and novel features of the innovation willbecome apparent from the following detailed description of theinnovation when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure are understood from the following detaileddescription when read with the accompanying drawings. It will beappreciated that elements, structures, etc. of the drawings are notnecessarily drawn to scale.

Accordingly, the dimensions of the same may be arbitrarily increased orreduced for clarity of discussion, for example.

FIG. 1 illustrates an example component diagram of a system according toaspects of the present innovation.

FIG. 2 illustrates an example component diagram of an out of officecomponent.

FIG. 3 illustrates an example component diagram of management component.

FIG. 4 illustrates an example component diagram of a determinationcomponent.

FIG. 5 illustrates an example method for out of office management.

FIG. 6 illustrates an example method for out of office management.

FIG. 7 illustrates a computing environment where one or more of theprovisions set forth herein can be implemented, according to someembodiments.

DETAILED DESCRIPTION

Out of office management is provided to an institution. Employee datacan be monitored of one or more employees of the institution. Alikelihood an employee is out of office is determined. A set of projectsof the employee that are affected by the out of office is determined.The projects are analyzed to determine departure scores for each projectof the set of projects. The projects are ranked according to thedeparture scores to determine criticality of each project. The projectsare reassigned based on the employee data and the ranking. Customersentiments are monitored after reassignment for machine learning toaffect future out of office reassignments.

Various aspects of the subject disclosure are now described in moredetail with reference to the annexed drawings, wherein like numeralsgenerally refer to like or corresponding elements throughout. It shouldbe understood, however, that the drawings and detailed descriptionrelating thereto are not intended to limit the claimed subject matter tothe particular form disclosed. Rather, the intention is to incorporateall modifications, equivalents, and alternatives falling within thespirit and scope of this specification and claims appended hereto.

FIG. 1 illustrates a system 100 for out of office management. The system100 includes a data component 110. The data component 110 monitorsemployee data of one or more employees of an institution. The datacomponent 110 monitors or otherwise receives employee data. For example,the data component 110 can monitor a first employee 120 and a secondemployee 130. For purposes of ease of explanation, the example system100 is described using two employees. However, it is contemplated that aplurality of employees can be monitored for employee data and use withthe system 100. The employee data can include at least one of employeecommunications data, calendar entries, time entries, social media,learned employee behavior, time off requests, and/or the like. In otheraspects, contextual data (e.g., current location, current activity,etc.) can be included in the employee data for analysis within thespirit and scope of the innovation. In one example, the data component110 can collect email correspondence about a project between an employeeand a customer thereby analyzing the content therein.

The system 100 includes an out of office component 140. The out ofoffice component 140 determines whether an employee is out of office. Insome embodiments, the out of office component 140 can determine aprobability or a binary determination that an employee is out of officebased on the employee data. The out of office component 140 receives andstores the employee data from the data component 110. From the monitoredemployee data, the out of office component 140 can determine alikelihood that an employee is out of the office. The out of officecomponent 140 analyzes the employee data to determine a likelihood theemployee is out of office. In some embodiments, the out of officecomponent 140 factors vacation days remaining, time of the year, season,upcoming holidays, and/or the like to predict the likelihood theemployee is out of office.

In some embodiments, the out of office component 140 can apply naturallanguage processing techniques to employee communications to look forwords or phrases that indicate the employee is out of office or going tobe out of office. For example, an employee email can be analyzed forwords such as “vacation,” “holiday,” and/or associated dates to factorinto the likelihood the employee is or will be out of office.

In other embodiments, the out of office component 140 can apply a volumemetric analysis to employee communications of the employee data todetermine the likelihood. For example, a sudden downturn or absence ofemails sent by an employee factors into a higher likelihood that theemployee is out of office. In some embodiments, the out of officecomponent 140 can determine an average score, weighted average score,normalized score or rating of each different employee data source todetermine an overall likelihood that the employee is out of the office.

In some embodiments, the out of office component 140 can compare thedetermined likelihood to an out of office threshold. The out of officethreshold can be a predetermined threshold percentage or score, where ifthe determined likelihood meets or exceeds the threshold, the out ofoffice component determines that the employee is out of the office. Insome embodiments, there are multiple thresholds that can translate todifferent actions or determinations. For example, meeting a lowerthreshold can trigger an automatic message to the employee to querywhether they are out of the office.

If the out of office component 140 determines that the employee is outof the office, the out of office component 140 determines a set ofprojects of the employee that are affected by the out of office. Theproject data can be retrieved by the data component 110. The projectscan be related to customer services or related to a customer of theinstitution. For example, the institution can be a financial institutionand the customer can be working with an employee towards a home loan.The home loan would be the project associated with the employee that isdetermined by the out of office component 140. In some embodiments, theout of office component 140 can determine projects using the analysis ofemployee communications and/or other employee data such as case numbers,files, and/or the like.

The out of office component 140 analyzes the projects to determine apriority for each project of the set of projects. In some embodiments,the priority is determined by a departure score. The out of officecomponent 140 determines a departure score for at least one project inthe set of projects. The departure score represents the risk that aproject may leave the institution if not attended to while the employeeis out of office.

The out of office component 140 determines project information of aproject or set of projects. The project information can include at leastone of customer information, customer status (e.g. new vs. existing),customer communications, stage of the project, urgency of project, loanrisk, customer sentiment, complaints data, historical departure score ofemployee, historical departure rate of employee, historical success rateof employee, tenure of employee, compliance data, competitor comparison,and/or the like. The project information is used to determine thedeparture score of the project to facilitate determining a priority ofprojects while the employee is out of office.

In some embodiments, each piece of project information can be assigned ascore and the scores can be aggregated into an overall departure score.The overall departure score can be an average, weighted average, total,and/or the like. In other embodiments, the out of office component 140uses and/or develops a model to determine the departure score. The modelcan include determined variables or constants for each type of projectinformation to determine the departure score.

In some embodiments, the out of office component 140 compares thedeparture score to a threshold departure score. The threshold departurescore can be predetermined, machine learned, and/or the like. In otherembodiments, the out of office component 140 compares the departurescore to multiple threshold departure scores to determine a priority ofthe project. In some embodiments, the out of office component 140 ranksmultiple projects by departure score to determine a priority. In otherembodiments, the out of office component 140 ranks the projects thatmeet the threshold departure score.

The out of office component 140 can reassign the projects based on theanalysis and the priority. The out of office component 140 can identifyone or more employees that are in office using analyzed employee datafrom the data component 110. The out of office component 140 use factorsin employee data of candidate employees to reassign a project to. Thefactors can include availability, presence within same division and/orlocation, historical performance, existing workload, attributes,historical departure score, timeliness in resolutions, and/or the like.The out of office component 140 can distribute the one or more projectsthat are prioritized to the one or more employees in office.

In some embodiments, the out of office component 140 can determine anoptimal employee to assign the projects. For example, the out of officecomponent 140 can use prediction models for distribution of projects toan optimal employee. In other embodiments, the out of office component140 uses advanced machine learning techniques to previous projects andother data to cluster prioritized customers or projects.

In some embodiments, the out of office component 140 can generate anotification or assignment message to the reassigned employee. Theassignment message can include automatically generated highlights of theproject history based on analysis of the monitored employee information.In some embodiments, the assignment message can include complaintsinformation.

In some embodiments, the data component 110 can monitor a customersentiment of a reassigned project. For example, the data component 110can monitor communications with a customer of a reassigned project todetermine whether the customer is satisfied with the project progressand/or completion. The monitored data can be used with machine learningconcepts to affect selection of employees to distribute futurereassigned projects.

FIG. 2 illustrates an example component diagram of an out of officecomponent 140. The out of office component 140 includes a monitorcomponent 210. The monitor component 210 monitors employee data of oneor more employees of an institution. The employee data can include atleast one of employee communications data, calendar entries, timeentries, social media, learned employee behavior, time off requests,and/or the like. For example, the monitor component 210 can collectemail correspondence about a project between an employee and a customer.

The out of office component 140 includes a management component 220. Themanagement component 220 determines whether an employee is out ofoffice. The management component 220 receives and stores the employeedata from the monitor component 210. From the monitored employee data,the management component 220 can determine a likelihood that an employeeis out of the office. The management component 220 analyzes the employeedata to determine a likelihood the employee is out of office. In someembodiments, the management component 220 uses vacation days remaining,time of the year, season, upcoming holidays, and/or the like to predictthe likelihood the employee is out of office.

In some embodiments, the management component 220 can apply naturallanguage processing techniques to employee communications to look forwords or phrases that indicate the employee is out of office or going tobe out of office. For example, an employee email can be analyzed forwords such as “vacation,” “holiday,” and/or associated dates to factorinto the likelihood the employee is or will be out of office.

In other embodiments, the management component 220 can apply a volumemetric analysis to employee communications of the employee data todetermine the likelihood. For example, a sudden downturn or absence ofemails sent by an employee factors into a higher likelihood that theemployee is out of office. In some embodiments, the management component220 can determine an average score, weighted average score, normalizedscore or rating of each different employee data source to determine anoverall likelihood that the employee is out of the office.

In some embodiments, the management component 220 can compare thedetermined likelihood to an out of office threshold. The out of officethreshold can be a predetermined threshold percentage or score, where ifthe determined likelihood meets or exceeds the threshold, the managementcomponent determines that the employee is out of the office. In someembodiments, there are multiple thresholds that can translate todifferent actions or determinations. For example, meeting a lowerthreshold can trigger an automatic message to the employee to querywhether they are out of the office.

The out of office component 140 includes a workload component 230. Ifthe management component 220 determines that the employee is out of theoffice, the workload component 230 determines a set of projects of theemployee that are affected by the out of office. The projects arerelated to customer services related to a customer of the institution.For example, the institution can be a financial institution and thecustomer can be working with an employee towards a home loan. The homeloan would be the project associated with the employee that isdetermined by the workload component 230. In some embodiments, theworkload component 230 can determine projects using the analysis ofemployee communications and/or other employee data such as case numbers,files, and/or the like.

The out of office component 140 includes a determination component 240.The determination component 240 analyzes the projects to determine apriority for each project of the set of projects. In some embodiments,the priority is determined by a departure score. The determinationcomponent 240 determines a departure score for at least one project inthe set of projects. The departure score represents the risk that aproject may leave the institution if not attended to while the employeeis out of office.

The determination component 240 determines project information of aproject or set of projects. The project information can include at leastone of customer information, customer status (e.g. new vs. existing),customer communications, stage of the project, urgency of project, loanrisk, customer sentiment, complaints data, historical departure score ofemployee, historical departure rate of employee, historical success rateof employee, tenure of employee, compliance data, competitor comparison,and/or the like. The project information is used to determine thedeparture score of the project to facilitate determining a priority ofprojects while the employee is out of office.

In some embodiments, each piece of project information can be assigned ascore and the scores can be aggregated into an overall departure score.The overall departure score can be an average, weighted average, total,and/or the like. In other embodiments, the determination component 240uses and/or develops a model to determine the departure score. The modelcan include determined variables or constants for each type of projectinformation to determine the departure score.

In some embodiments, the determination component 240 compares thedeparture score to a threshold departure score. The threshold departurescore can be predetermined, machine learned, and/or the like. In otherembodiments, the determination component 240 compares the departurescore to multiple threshold departure scores to determine a priority ofthe project. In some embodiments, the determination component 240 ranksmultiple projects by departure score to determine a priority. In otherembodiments, the determination component 240 ranks the projects thatmeet the threshold departure score.

The out of office component 140 includes an assignment component 250.The assignment component 250 reassigns the projects based on theanalysis and the priority. The assignment component 250 and/or themanagement component 220 can identify one or more employees that are inoffice using analyzed employee data from the management component 220.The assignment component 250 use factors in employee data of candidateemployees to reassign a project to. The factors can includeavailability, presence within same division and/or location, historicalperformance, existing workload, attributes, historical departure score,timeliness in resolutions, and/or the like. The assignment component 250can distribute the one or more projects that are prioritized to the oneor more employees in office.

In some embodiments, the assignment component 250 can determine anoptimal employee to assign the projects. For example, the assignmentcomponent 250 can use prediction models for distribution of projects toan optimal employee. In other embodiments, the assignment component 250uses advanced machine learning techniques to previous projects tocluster prioritized customers or projects.

In some embodiments, the assignment component 250 can generate anotification or assignment message to the reassigned employee. Theassignment message can include automatically generated highlights of theproject history based on analysis of the monitored employee information.In some embodiments, the assignment message can include complaintsinformation.

In some embodiments, the monitor component 210 can monitor a customersentiment of a reassigned project. For example, the monitor component210 can monitor communications with a customer of a reassigned projectto determine whether the customer is satisfied with the project progressand/or completion. The monitored data can be used with machine learningconcepts to affect selection of employees to distribute futurereassigned projects.

FIG. 3 illustrates a detailed component diagram of the managementcomponent 220. The management component 220 includes a data component310. The data component 310 receives and stores the employee data fromthe monitor component 210. The management component 220 includes ananalysis component 320. From the monitored employee data, the analysiscomponent 320 can determine a likelihood that an employee is out of theoffice. The analysis component 320 analyzes the employee data todetermine a likelihood the employee is out of office.

In some embodiments, the analysis component 320 can apply naturallanguage processing techniques to employee communications to look forwords or phrases indicating the employee is out of office or going to beout of office. For example, an employee email can be analyzed for wordssuch as “vacation,” “holiday,” and/or associated dates to factor intothe likelihood the employee is or will be out of office.

In other embodiments, the analysis component 320 can apply a volumemetric analysis to employee communications of the employee data todetermine the likelihood. For example, a sudden downturn or absence ofemails sent by an employee factors into a higher likelihood that theemployee is out of office. In some embodiments, the analysis component320 can determine an average score, weighted average score, normalizedscore or rating of each different employee data source to determine anoverall likelihood that the employee is out of the office.

In some embodiments, the management component 220 includes a comparisoncomponent 330. The comparison component 330 can compare the determinedlikelihood to an out of office threshold. The out of office thresholdcan be a predetermined threshold percentage or score, where if thedetermined likelihood meets or exceeds the threshold, the managementcomponent 220 determines that the employee is out of the office. In someembodiments, there are multiple thresholds that can translate todifferent actions or determinations. For example, meeting a lowerthreshold can trigger an automatic message to the employee to querywhether they are out of the office.

FIG. 4 illustrates a component diagram of the determination component240. The determination component 240 analyzes the projects to determinea priority for each project of the set of projects. In some embodiments,the priority is determined by a departure score. The determinationcomponent 240 includes a scoring component 410. The scoring component410 determines a departure score for at least one project in the set ofprojects. The departure score represents the risk that a project mayleave the institution if not attended to while the employee is out ofoffice.

The determination component 240 includes an information component 420.The information component 420 determines project information of aproject or set of projects. The project information can include at leastone of customer information, customer communications, stage of theproject, customer sentiment, complaints data, and/or the like. Theproject information is used to determine the departure score of theproject to facilitate determining a priority of projects while theemployee is out of office.

In some embodiments, the scoring component 410 assigns each piece ofproject information a score and aggregates the scores into an overalldeparture score. The overall departure score can be an average, weightedaverage, total, and/or the like. In other embodiments, the determinationcomponent 240 can include a modeling component 430. The modelingcomponent 430 uses and/or develops a model to determine the departurescore. The model can include determined variables or constants for eachtype of project information to determine the departure score.

The determination component 240 includes a threshold component 440. Thethreshold component 440 compares the departure score to a thresholddeparture score. The threshold departure score can be predetermined,machine learned, and/or the like. In other embodiments, the thresholdcomponent 440 compares the departure score to multiple thresholddeparture scores to determine a priority of the project. In someembodiments, the scoring component 410 ranks multiple projects bydeparture score to determine a priority. In other embodiments, thescoring component 410 ranks the projects that meet the thresholddeparture score.

The aforementioned systems, architectures, platforms, environments, orthe like have been described with respect to interaction between severalcomponents. It should be appreciated that such systems and componentscan include those components or sub-components specified therein, someof the specified components or sub-components, and/or additionalcomponents. Sub-components could also be implemented as componentscommunicatively coupled to other components rather than included withinparent components. Further yet, one or more components and/orsub-components may be combined into a single component to provideaggregate functionality. Communication between systems, componentsand/or sub-components can be accomplished in accordance with either apush and/or pull control model. The components may also interact withone or more other components not specifically described herein for sakeof brevity, but appreciated by those of skill in the art.

Furthermore, various portions of the disclosed systems above and methodsbelow can include or employ artificial intelligence, machine learning,or knowledge or rule-based components, sub-components, processes, means,methodologies, or mechanisms (e.g., support vector machines, neuralnetworks, expert systems, Bayesian belief networks, fuzzy logic, datafusion engines, classifiers. . .). Among other things, such componentscan automate certain mechanisms or processes performed thereby to makeportions of the systems and methods more adaptive as well as efficientand intelligent. By way of example, and not limitation, such mechanismscan be utilized by the out of office component 140 for out of officemanagement.

In view of the example systems described above, methods that may beimplemented in accordance with the disclosed subject matter will bebetter appreciated with reference to flow chart diagram of FIGS. 5 and 6. While for purposes of simplicity of explanation, the methods are shownand described as a series of blocks, it is to be understood andappreciated that the disclosed subject matter is not limited by theorder of the blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Moreover, not all illustrated blocks may be required toimplement the methods described hereinafter. Further, each block orcombination of blocks can be implemented by computer programinstructions that can be provided to a processor to produce a machine,such that the instructions executing on the processor create a means forimplementing functions specified by a flow chart block.

FIG. 5 illustrates a method 500 for out of office management. At 510,employee data is monitored. The employee data can be communicationsdata, vacation requests, employee calendars, and/or the like. Theemployee data can be collected by a data component 110. At 520, adetermination that an employee is out of office is made. Thedetermination is based on an analysis of the monitored employee data. Anout of office component 140 can analyze the employee data received fromthe data component 110. At 530, a set of projects of the out of officeemployee is determined. The set of projects can be all projectsdetermined to be affected by the period the employee is out of office orall projects associated with the employee. A workload component 230 cananalyze employee data and determine affected projects.

At 540, a departure score is determined for the set of projects. Adetermination component 240 can determine the departure score. Thedeparture score represents the likelihood that a customer associatedwith a particular project in the set of projects may remove the projectfrom the institution. For example, a home loan project with a due datefalling during an assigned employee's out of office period can translateto a high departure score as the likelihood the home loan project goesto a competitor is high if not handled on time. At 550, projects arereassigned to another employee(s) according to the departure score. Anassignment component 250 can reassign the projects. In the example, thehigh departure score for the home loan leads to the home loan beingreassigned to a different employee at the same financial institution tokeep the customer from moving the home loan to a competitor.

FIG. 6 illustrates a method 600 for out of office management. At 605,data is aggregated about employees and/or projects. For example,employee data can be aggregated such as emails, texts, activity volumedelta, computer data, vacation requests, expected holidays, birthdays,machine learned probable vacation times, and/or the like. A datacomponent 110 can aggregate the data. At 610, the employee data ismonitored for vacation triggers. In some embodiments, an out of officecomponent 140 can utilize natural language processing and/or textanalysis in email monitoring models. In other embodiments, the out ofoffice component 140 can utilize attribution analysis to assign weightsto certain events that could lead to an out of office determination. Theout of office component 140 can predict the probability of out of officefor an employee based on used/unused vacation days, time of year,season, position of employee within the institution, holidays, employeebirthday/anniversary, and/or the like. In some embodiments, the out ofoffice component 140 can use regular expressions to detect an out ofoffice subject line. In other embodiments, the out of office component140 can utilize a logistic regression model using a specific phrasesearch in emails or other communications. At 615, a determination ofwhether a vacation or out of office trigger is detected. If no triggeris detected, the method 600 returns to 605 to continue data aggregationand 610 to monitor for triggers. In some embodiments, the out of officecomponent 140 can predict when an employee will be out of office in thefuture and trigger actions based on the prediction.

If a trigger is detected, at 620, a departure score is determined forprojects of the out of office employee or team member. A determinationcomponent 240 can determine the departure score. The departure scorerepresents the likelihood that a customer associated with a particularproject may remove the project from the institution. For example, a homeloan project with a due date falling during an assigned employee's outof office period can translate to a high departure score as thelikelihood the home loan project goes to a competitor is high if nothandled on time. In some embodiments, the determination component 240can create modeling features such as stage of the project/loan,project/loan risk, employee current status, and/or the like. In someembodiments, the determination component 240 can identify urgency of aproject using natural language processing and/or text analysis withcustomer conversation references to time or other institutions. In someembodiments, the determination component 240 can determine customersentiments from the analysis to factor into the departure score. In someembodiments, the determination component 240 can use complaints data andmachine learning to factor into the departure score.

In some embodiments, the departure score is calculated based on atwo-step modeling process. An unstructured process may be followed by astructured process. The output of these two processes combined woulddetermine the probability of departure which will be further translatedinto a departure score with a specified range. The unstructuredinformation may include but not limited to: historical departure score,email/text conversation with customer, customer sentiment analysis, loanurgency, other bank comparisons, and/or the like. The structureinformation may include customer attributes such as new vs. existingcustomer, other products associated with the institution, loan valueassociated with the customer, and/or the like. The structuredinformation may include loan attributes such as type of loan, loanvalue, loan urgency, stage of loan, and/or the like. The structuredinformation may include property attributes such as property value,location, and/or the like. The structured information may include teammember attributes such as historical success rate, loans closed vs.loans lost, tenure/experience, complaints against, non-compliancefactors, incentives, and/or the like. A modeling technique uses theunstructured information and structured information to refine thedeparture score. The modeling techniques may include text analysis andclassification techniques such as Ngrams, Adaboost, sentiment analysis,SVM, NB, deep learning, AI, and/or the like.

At 625, a determination is made of the criticality of each affectedproject. The determination can be made by a determination component 240based on the departure score. If the project, e.g. a loan, is determinedto be critical, e.g. has a high departure score, the project is given ahigh priority status at 630. If the project is determined to be lesscritical, e.g. has a lower departure score, the project is given a lowpriority status at 635. The priority status affects how the projects areassigned or reassigned because of the out of office employee to bestkeep the projects from being taken away by the customer.

At 640, project continuity is channeled, e.g. an optimal projectredistribution is initiated. A high priority project may be determinedto need to be reassigned so that the project is retained with theinstitution. An assignment component 250 may determine theredistribution. In some embodiments, the assignment component 250 usesadvanced machine learning techniques to cluster prioritized projectsand/or customers. In some embodiments, based on the departure score,high priority projects are prioritized and redistributed in two ways:rerouting the project to a group of team members in the samedivision/location and are in office as the out of office employee or adifferent group that can handle the same project.

At 645, a team member is identified for redistribution. The assignmentcomponent 240 can identify employees or team members that are not out ofoffice, e.g. “are in office”. In some embodiments, the assignmentcomponent 240 can use prediction models, optimization models, and/or thelike for redistribution of projects/loans to the most optimal orappropriate team member. The assignment component 140 can utilize teammember factors such as availability (in office or workload), presencewithin same division/location, historical performance, team memberattributes (described above), departure score based on loans handled,timeliness in resolution, and/or the like.

At 650, a group notification can be generated and addressed. The groupnotification may optionally be in addition to or in replace ofidentifying the team member at 645. The group notification can includethe out of office employee, an in-office employee, and/or an entireteam. For example, a loan team may be addressed for the notification.The notification can include highlights or details of the high priorityproject to be redistributed to the team or individual team member. Insome embodiments, the assignment component 250 can use text analysis toprovide the information for the notification. In some embodiments, thegroup notification can addressed to selected team members, the selectionis based on team member availability, use of loan attributes, customerattributes, email history, and/or the like.

At 655, a project history is provided to the team and/or the individualteam member. The project history can include customer correspondencewith the out of office employee, customer complaints history, and/orother project historical data. At 660, incentives may be provided to theteam and/or individual team member. The incentives may optionally be inaddition to or in replace of providing the project history at 655. Theincentives provided may be bonuses, shared commission, extra vacationdays, and/or the like to reward a team member that completes a highprioritized project that is retained by the institution.

At 665, a determination is made whether the high prioritized project istransitioned successfully to another team member. If the project was notsuccessfully transitioned, the method 600 can iterate back to 645/655 tofind another team member to transition the project to. If the project istransitioned successfully, at 670, customer sentiment and urgency of theproject is monitored through completion of the project. The datacomponent 110 can monitor communications between the in-office teammember and the customer to determine sentiment of the customer. In someembodiments, text handlers may be used to identify return date of theout of office employee and send customized replies to customers usingcontext if project is transitioned back to the out of office employee.In other embodiments, a loan conversion ration comparison is determinedfor before and after a redistribution of a project. In some embodiments,a distributed ledger, e.g. blockchain and/or the like, may be used tostore project transitions between employees.

The innovation disclosed and claimed herein, in one aspect thereof,comprises systems and methods of out of office management. Themanagement can include monitoring employee data of one or more employeesof an institution. An employee is determined to be out of office. A setof projects of the employee that are affected by the out of office isdetermined. The projects are related to customer services a customer ofthe institution. The projects are analyzed to determine a priority foreach project of the set of projects. The projects are reassigned basedon the analysis and the priority.

A system of the innovation can include a monitor component that monitorsemployee data of one or more employees of an institution. An managementcomponent determines an employee is out of office. A workload componentdetermines a set of projects of the employee that are affected by theout of office, wherein the projects are related to customer services acustomer of the institution. A determination component analyzes theprojects to determine a priority for each project of the set ofprojects. An assignment component reassigns the projects based on theanalysis and the priority.

A computer readable medium has instructions to control one or moreprocessors. The instructions can include monitoring employee data of oneor more employees of an institution. The instructions can includedetermining a likelihood an employee is out of office and comparing thelikelihood to a threshold to determine the employee is out of office.The instructions can include determining a set of projects of theemployee that are affected by the out of office, wherein the projectsare related to customer services a customer of the institution. Theinstructions can include analyzing the projects to determine departurescores for each project of the set of projects. The instructions caninclude ranking each project according to the departure scores. Theinstructions can include reassigning the projects based on the employeedata and the ranking.

As used herein, the terms “component” and “system,” as well as variousforms thereof (e.g., components, systems, sub-systems. . .) are intendedto refer to a computer-related entity, either hardware, a combination ofhardware and software, software, or software in execution. For example,a component may be, but is not limited to being, a process running on aprocessor, a processor, an object, an instance, an executable, a threadof execution, a program, and/or a computer. By way of illustration, bothan application running on a computer and the computer can be acomponent. One or more components may reside within a process and/orthread of execution and a component may be localized on one computerand/or distributed between two or more computers.

The conjunction “or” as used in this description and appended claims isintended to mean an inclusive “or” rather than an exclusive “or,” unlessotherwise specified or clear from context. In other words, “‘X’ or ‘Y’”is intended to mean any inclusive permutations of “X” and “Y.” Forexample, if “‘A’ employs ‘X,’” “‘A ’ employs ‘Y,’” or “‘A’ employs both‘X’ and ‘Y,’” then “‘A’ employs ‘X’ or ‘Y’” is satisfied under any ofthe foregoing instances.

Furthermore, to the extent that the terms “includes,” “contains,” “has,”“having” or variations in form thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

To provide a context for the disclosed subject matter, FIG. 7 as well asthe following discussion are intended to provide a brief, generaldescription of a suitable environment in which various aspects of thedisclosed subject matter can be implemented. The suitable environment,however, is solely an example and is not intended to suggest anylimitation as to scope of use or functionality.

While the above disclosed system and methods can be described in thegeneral context of computer-executable instructions of a program thatruns on one or more computers, those skilled in the art will recognizethat aspects can also be implemented in combination with other programmodules or the like. Generally, program modules include routines,programs, components, data structures, among other things that performparticular tasks and/or implement particular abstract data types.Moreover, those skilled in the art will appreciate that the abovesystems and methods can be practiced with various computer systemconfigurations, including single-processor, multi-processor ormulti-core processor computer systems, mini-computing devices, servercomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant (PDA), smart phone, tablet, watch. ..), microprocessor-based or programmable consumer or industrialelectronics, and the like. Aspects can also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. However, some,if not all aspects, of the disclosed subject matter can be practiced onstand-alone computers. In a distributed computing environment, programmodules may be located in one or both of local and remote memorydevices.

With reference to FIG. 7 , illustrated is an example computing device700 (e.g., desktop, laptop, tablet, watch, server, hand-held,programmable consumer or industrial electronics, set-top box, gamesystem, compute node. . .). The computing device 700 includes one ormore processor(s) 710, memory 720, system bus 730, storage device(s)740, input device(s) 750, output device(s) 760, and communicationsconnection(s) 770. The system bus 730 communicatively couples at leastthe above system constituents. However, the computing device 700, in itssimplest form, can include one or more processors 710 coupled to memory720, wherein the one or more processors 710 execute various computerexecutable actions, instructions, and or components stored in the memory720.

The processor(s) 710 can be implemented with a general-purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general-purpose processor maybe a microprocessor, but in the alternative, the processor may be anyprocessor, controller, microcontroller, or state machine. Theprocessor(s) 710 may also be implemented as a combination of computingdevices, for example a combination of a DSP and a microprocessor, aplurality of microprocessors, multi-core processors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration. In one embodiment, the processor(s) 710 can be a graphicsprocessor unit (GPU) that performs calculations with respect to digitalimage processing and computer graphics.

The computing device 700 can include or otherwise interact with avariety of computer-readable media to facilitate control of thecomputing device to implement one or more aspects of the disclosedsubject matter. The computer-readable media can be any available mediathat accessible to the computing device 700 and includes volatile andnonvolatile media, and removable and non-removable media.Computer-readable media can comprise two distinct and mutually exclusivetypes, namely storage media and communication media.

Storage media includes volatile and nonvolatile, removable, andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules, or other data. Storage media includes storage devicessuch as memory devices (e.g., random access memory (RAM), read-onlymemory (ROM), electrically erasable programmable read-only memory(EEPROM). . .), magnetic storage devices (e.g., hard disk, floppy disk,cassettes, tape. . .), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD). . .), and solid state devices (e.g., solid statedrive (SSD), flash memory drive (e.g., card, stick, key drive. . .). ..), or any other like mediums that store, as opposed to transmit orcommunicate, the desired information accessible by the computing device700. Accordingly, storage media excludes modulated data signals as wellas that described with respect to communication media.

Communication media embodies computer-readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, radio frequency (RF), infrared and other wireless media.

The memory 720 and storage device(s) 740 are examples ofcomputer-readable storage media. Depending on the configuration and typeof computing device, the memory 720 may be volatile (e.g., random accessmemory (RAM)), non-volatile (e.g., read only memory (ROM), flash memory.. .) or some combination of the two. By way of example, the basicinput/output system (BIOS), including basic routines to transferinformation between elements within the computing device 700, such asduring start-up, can be stored in nonvolatile memory, while volatilememory can act as external cache memory to facilitate processing by theprocessor(s) 710, among other things.

The storage device(s) 740 include removable/non-removable,volatile/non-volatile storage media for storage of vast amounts of datarelative to the memory 720. For example, storage device(s) 740 include,but are not limited to, one or more devices such as a magnetic oroptical disk drive, floppy disk drive, flash memory, solid-state drive,or memory stick.

Memory 720 and storage device(s) 740 can include, or have storedtherein, operating system 780, one or more applications 786, one or moreprogram modules 784, and data 782. The operating system 780 acts tocontrol and allocate resources of the computing device 700. Applications786 include one or both of system and application software and canexploit management of resources by the operating system 780 throughprogram modules 784 and data 782 stored in the memory 720 and/or storagedevice(s) 740 to perform one or more actions. Accordingly, applications786 can turn a general-purpose computer 700 into a specialized machinein accordance with the logic provided thereby.

All or portions of the disclosed subject matter can be implemented usingstandard programming and/or engineering techniques to produce software,firmware, hardware, or any combination thereof to control the computingdevice 700 to realize the disclosed functionality. By way of example andnot limitation, all or portions of the out of office component 140 canbe, or form part of, the application 786, and include one or moremodules 784 and data 782 stored in memory and/or storage device(s) 740whose functionality can be realized when executed by one or moreprocessor(s) 710.

In accordance with one particular embodiment, the processor(s) 710 cancorrespond to a system on a chip (SOC) or like architecture including,or in other words integrating, both hardware and software on a singleintegrated circuit substrate. Here, the processor(s) 710 can include oneor more processors as well as memory at least similar to theprocessor(s) 710 and memory 720, among other things. Conventionalprocessors include a minimal amount of hardware and software and relyextensively on external hardware and software. By contrast, an SOCimplementation of processor is more powerful, as it embeds hardware andsoftware therein that enable particular functionality with minimal or noreliance on external hardware and software. For example, the out ofoffice component 140 and/or functionality associated therewith can beembedded within hardware in a SOC architecture.

The input device(s) 750 and output device(s) 760 can be communicativelycoupled to the computing device 700. By way of example, the inputdevice(s) 750 can include a pointing device (e.g., mouse, trackball,stylus, pen, touch pad. . .), keyboard, joystick, microphone, voice userinterface system, camera, motion sensor, and a global positioningsatellite (GPS) receiver and transmitter, among other things. The outputdevice(s) 760, by way of example, can correspond to a display device(e.g., liquid crystal display (LCD), light emitting diode (LED), plasma,organic light-emitting diode display (OLED). . .), speakers, voice userinterface system, printer, and vibration motor, among other things. Theinput device(s) 750 and output device(s) 760 can be connected to thecomputing device 700 by way of wired connection (e.g., bus), wirelessconnection (e.g., Wi-Fi, Bluetooth. . .), or a combination thereof.

The computing device 700 can also include communication connection(s)770 to enable communication with at least a second computing device 702by means of a network 790. The communication connection(s) 770 caninclude wired or wireless communication mechanisms to support networkcommunication. The network 790 can correspond to a local area network(LAN) or a wide area network (WAN) such as the Internet. The secondcomputing device 702 can be another processor-based device with whichthe computing device 700 can interact. For example, the computing device700 can correspond to a server that executes functionality of out ofoffice component 140, and the second computing device 702 can be a userdevice that communications and interacts with the computing device 700.

What has been described above includes examples of aspects of theclaimed subject matter. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but one of ordinary skill in theart may recognize that many further combinations and permutations of thedisclosed subject matter are possible. Accordingly, the disclosedsubject matter is intended to embrace all such alterations,modifications, and variations that fall within the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method, comprising: monitoring, by a processor,employee data of one or more employees of an institution; determining,by the processor, a likelihood that an employee is out of office basedupon analyzing the employee data; determining, by the processor, a setof projects of the employee that are affected by the out of office,wherein the set of projects are related to customer services of theinstitution; analyzing, by the processor, the set of projects todetermine a priority for each project of the set of projects, whereindetermining the priority comprises: developing, by the processor, amodel based in part on a historical departure score, text analysis ofcustomer correspondence, and customer attributes; determining, by theprocessor, a departure score using the model for at least one project inthe set of projects, the departure score representing a risk a projectmay leave the institution; and detecting, by the processor, thedeparture scores of a subset of projects in the set of projects exceedsa threshold departure score; reassigning, by the processor, the subsetof projects in the set of projects based on the detection, the textanalysis, and the priority; determining, by the processor, afterreassigning the subset of projects, a second departure score based onthe model and updated text analysis of customer correspondence;detecting, by the processor, the second departure scores of the subsetof projects in the set of projects exceeds a threshold departure score;and reassigning, by the processor, the subset of projects in the set ofprojects based on the detection, the updated text analysis, and thepriority.
 2. The method of claim 1, wherein determining an employee isout of office, comprises: comparing the likelihood to an out of officethreshold.
 3. The method of claim 2, wherein the employee data includesat least one of employee communications data, calendar entries, timeentries, social media, learned employee behavior, contextual data ortime off requests.
 4. The method of claim 3, the analyzing the employeedata comprising: applying natural language processing and volume metricanalysis to employee communications data of the employee data todetermine the likelihood.
 5. The method of claim 1, wherein determiningthe departure score comprises: determining the project information theproject information including at least one of customer information,customer communications, stage of the project, customer sentiment, orcomplaints data.
 6. The method of claim 5, further comprising: modelingthe project information to determine the departure score using themodel.
 7. The method of claim 1, wherein the reassigning comprises:identifying one or more employees that are in office using analyzedemployee data; and distributing the one or more projects that are abovea priority threshold to the one or more employees in office.
 8. Themethod of claim 7, further comprising: monitoring customer sentiment ofa reassigned project.
 9. The method of claim 1, wherein the model isdeveloped in part by training a machine learning algorithm from previousprojects and out of office employee situations.
 10. A system,comprising: one or more processors configured to execute program codethat, when executed causes the one or more processors to: monitoremployee data of one or more employees of an institution; determine alikelihood that an employee is out of office based in part upon theemployee data; determine a set of projects of the employee that areaffected by the out of office, wherein the projects are related tocustomer services of the institution; analyze the set of projects todetermine a priority for each project of the set of projects, by:developing a model based in part on a historical departure score, textanalysis of customer correspondence, and customer attributes; determinea departure score for at least one project in the set of projects, thedeparture score representing a risk a project may leave the institution;detect the departure scores of a subset of projects in the set ofprojects exceeds a threshold departure score; reassign each of thesubset of projects in the set of projects based on the detection, thetext analysis, and the priority; determine, after reassigning the subsetof projects, a second departure score based on the model and updatedtext analysis of customer correspondence; detect the second departurescores of the subset of projects in the set of projects exceeds athreshold departure score; and reassign the subset of projects in theset of projects based on the detection, the updated text analysis, andthe priority.
 11. The system of claim 10, wherein determining theemployee is out of the office is further based on: comparing thelikelihood to an out of office threshold.
 12. The system of claim 11,wherein the one or more processors are further configured to: store theemployee data, wherein the employee data includes at least one ofemployee communications data, calendar entries, time entries, socialmedia, learned employee behavior, contextual data or time off requests.13. The system of claim 12, wherein the one or more processors arefurther configured to: apply natural language processing and volumemetric analysis to employee communications data of the employee data todetermine the likelihood.
 14. The system of claim 10, wherein the one ormore processors are further configured to: determine the projectinformation, the project information including at least one of customerinformation, customer communications, stage of the project, customersentiment, or complaints data.
 15. The system of claim 14, wherein theone or more processors are further configured to: model the projectinformation to determine the departure score.
 16. The system of claim10, wherein the one or more processors are further configured to:identify one or more employees that are in office using analyzedemployee data; and distribute the one or more projects that are above apriority threshold to the one or more employees in office.
 17. Thesystem of claim 10, wherein the one or more processors are furtherconfigured to: monitor a customer sentiment of a reassigned project. 18.The system of claim 10, wherein the model is developed in part bytraining a machine learning algorithm from previous projects and out ofoffice employee situations.
 19. A non-transitory computer readablemedium having instructions to control one or more processors configuredto: monitor employee data of one or more employees of an institution;determine a likelihood an employee is out of office based at least inpart upon the employee data compare the likelihood to a threshold todetermine the employee is out of office; determine a set of projects ofthe employee that are affected by the out of office, wherein theprojects are related to customer services a customer of the institution;develop a model based in part on a historical departure score, textanalysis of customer correspondence, and customer attributes; analyzethe set of projects according to the model to determine departure scoresfor each project of the set of projects; rank each project of the set ofprojects according to the departure scores; detect the departure scoreof a subset of projects in the set of projects exceeds a thresholddeparture score; and reassign the subset of projects based on theemployee data the detection, and the ranking; determine, afterreassigning the subset of projects, a second departure score based onthe model and updated text analysis of customer correspondence; detectthe second departure scores of the subset of projects in the set ofprojects exceeds a threshold departure score; and reassign the subset ofprojects in the set of projects based on the detection, the updated textanalysis, and the priority.
 20. The non-transitory computer readablemedium of claim 19, wherein the one or more processors are furtherconfigured to reassign each of the set of projects based on a secondmodel that determines an optimal employee for distribution of reassignedprojects.